1887

Abstract

Abstract

Various empirical observations have been made regarding the nature of the distribution of shale productivity and the potential existence of three dependent intervals that seem to separate the uneconomical wells from the average and good wells. The areas of good well productivity exhibit a log normal distribution and seem to be controlled mainly by the natural fracture system. These differences in performance seem to be related mostly to the shale capacity defined as the product of four key shale drivers: TOC, porosity, brittleness, and fracture density. When drilling a shale reservoir, it appears that the Relative Intercepted Shale Capacity (RISC) seems to have a strong correlation with the resulting relative well performance. Consequently, well productivity could be predicted in a relative sense with reasonable accuracy if the 3D shale capacity model is available to the operator. Having such a 3D model available, a shale operator can compute the RISC of his future wells and adjust their landing zone, azimuth and length accordingly to reduce drilling and fracing costs, to achieve the best return on investment and accelerate the time to payout.

Loading

Article metrics loading...

/content/papers/10.2118/167779-MS
2014-02-25
2024-04-19
Loading full text...

Full text loading...

References

  1. MIT
    , 2011, The future of natural gas, An interdisciplinary MIT study: 178 p. http://mitei.mit.edu/system/files/NaturalGas_Report.pdf
    [Google Scholar]
  2. Pearson, C.M., L.Griffin, C.Wright, and L.Weijers
    , “Breaking up is hard to do: Creating hydraulic fracture complexity in the Bakken central basin:”, paper SPE 163827 presented at 2013 SPE Hydraulic Fracturing Technology Conference, Feb 04–06, The Woodlands, TX, USA.
    [Google Scholar]
  3. Gale, J.F., R.M.Reed, S.P.Becker, and W.Ali
    , “Natural fractures in the Barnett Shale in the Delaware Basin, Pecos County, West Texas: Comparison with the Barnett Shale in the Fort Worth Basin:”, AAPG Search and Discovery Article #10226 (2010), http://www.searchanddiscovery.com/documents/2010/10226gale/ndx_gale.pdf
    [Google Scholar]
  4. Mitchell, G.C., and T.B.Berge
    , “Structural attribute analysis used in Barnett resource development:” AAPG Search and Discovery Article #110095 (2009).
    [Google Scholar]
  5. Swindell, G.
    , “Eagle Ford Shale-An early look at ultimate recovery:” paper SPE 158207 presented at the 2012 SPE ATCE, 8–10 October, San Antonio, Texas, USA
    [Google Scholar]
  6. Ouenes, A.
    , “Seismically driven characterization of unconventional shale plays:” CSEG Recorder, v.37/2 (February) 2012.
    [Google Scholar]
  7. Blood, D.R.
    , “Sequence stratigraphy crucial to lateral placement in Marcellus shale play:” The American Oil & Gas Reporter, v. 54/8, (August), 2011, p. 52–60.
    [Google Scholar]
  8. Ouenes, A.
    , “Practical application of fuzzy logic and neural networks to fractured reservoir characterization,” inShahabMohagegh (ed.), Computers and Geosciences, v. 26/7, 2000.
    [Google Scholar]
  9. Jenkins, C., A.Ouenes, A.Zellou, and J.Wingard
    , “Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models:” AAPG Bulletin, v.,. 93/11, 2009, p. 1597–1608.
    [Google Scholar]
  10. Chopra, S., Castagna, J., Portniaguine, O.
    : “Seismic resolution and thin-bed reflectivity inversion”, CSEG Recorder, (January) 2006.
    [Google Scholar]
  11. Whitcombe, D.
    , “Elastic impedance normalization,” Geophysics, 67, 2002, pp. 60–62.
    [Google Scholar]
  12. Whitcombe, D.N., Connolly, P.A., Reagan, R.L., and Redshaw, T.C.
    , “Extended elastic impedance for fluid and lithology prediction,” Geophysics, 67, 2002, pp. 63–67.
    [Google Scholar]
  13. Ouenes, A. et al.
    , “Integrated characterization and simulation of the fractured Tensleep reservoir at Teapot Dome for CO2 injection design:” paper SPE 132404 presented at the 2010 SPE Western Regional Meeting, 27–29 May, Anaheim, California, USA
    [Google Scholar]
  14. Ouenes, A., et al..
    : “Integrated Property and Fracture Modeling Using 2D Seismic Data: Application to an Algerian Cambrian Field” paper SPE 109272, presented at the 2007 SPE ATCE, Anaheim.
    [Google Scholar]
  15. Ouenes, A., Bachir, A., Boukhelf, D.
    : “Estimation of Stimulated Reservoir Volume Using the Concept of Shale Capacity and its Validation with Microseismic and Well Performance”, paper SPE 167778, presented at the 2014 SPE/EAGE European Unconventional Conference and Exhibition, Vienna, Austria, 25–27 February.
    [Google Scholar]
  16. Reagan, J., WojcikE., Fackler, M., Ouenes, A.
    : “Predicting Well Performances Using the Shale Capacity Concept: Application to the Haynesville”, AAPG Search and Discovery Article # 41204 (2013).
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.2118/167779-MS
Loading
/content/papers/10.2118/167779-MS
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error